AI Operations Engineer

About the Role We are looking for an AI Ops Engineer to help build and run a new AI Ops function at ClickHouse. You will work under a Technical Lead to design, build, and maintain AI-driven solutions across the business (People, Finance, Legal, and beyond) and engineering — building integrations, maintaining agents, supporting cost visibility, and helping train the org on AI best practices. This is a hands-on execution role: you will move quickly, ship real workflows, and help the team learn what works as we centralize AI Ops for the first time. Success This is a net-new function; specific goals will be set with your Technical Lead. Directionally, the team is measured on: Measurable reduction in redundant or inefficient AI tool and model spend through right-sizing, routing, and cost visibility A working framework for evaluating and selecting models/tools by cost, latency, accuracy, and risk, adopted across teams Agents and AI workflows built, deployed, and maintained cross-functionally (People, Finance, Legal, Engineering, and beyond) Improved AI fluency and literacy org-wide, measured through training participation, adoption of approved tools, and reduced shadow-AI usage Established identity, access, audit, and lifecycle practices for AI agents that satisfy Security and GRC requirements You Will Build AI-Powered Solutions Develop and deploy solutions using LLMs, automation frameworks, and internal tooling, for both business and engineering use cases Integrate AI into existing systems (HRIS, ATS, ERP, procurement orchestration, ticketing, CI/CD, developer tooling) and legal infrastructure like CLM or Matter Management Build workflows such as document generation, data extraction, knowledge retrieval, and decision support across systems and knowledge bases Use modern integration standards (e.g., MCP) to give AI models and agents direct, secure access to internal knowledge and context Support Model Evaluation Help test and benchmark AI models against accuracy, latency, cost, and safety criteria Support model risk assessments and due diligence alongside Security and GRC Help implement and maintain the internal model routing solution Support Cost Visibility Help build dashboards and reporting for AI/LLM spend across teams and tools Implement tagging, monitoring, and alerting for cost anomalies Identify and execute on cost-optimization opportunities (prompt efficiency, caching, model right-sizing) Maintain Agent Infrastructure Help provision, credential, and de-provision AI agent access Support deployment, versioning, monitoring, and retirement of agents Maintain and patch agent infrastructure and dependencies Help maintain audit trails and logging for agent actions Deliver Training and Enablement Design and deliver company-wide AI training programs tailored to different audiences — non-technical business users through engineers Create lightweight playbooks, guides, templates, and reusable components for safe, effective AI adoption Run onboarding sessions, office hours, and workshops to build AI fluency and drive adoption of approved tools and agents Establish a feedback loop with users to continuously improve training content, tooling, and documentation You Bring You are not expected to have every skill below — tell us which are your strongest. Experience building with modern AI/ML tools and frameworks Experience building and consuming APIs, developing data pipelines, and architecting system integrations Working knowledge of cloud infrastructure (AWS, GCP, or Azure) and experience hosting or operating services in production Experience comparing and evaluating AI models across providers, including trade-offs between cost, latency, and quality Comfort working with usage and billing data to build cost visibility, forecasting, or optimization Ability to understand both business and engineering workflows and translate them into technical solutions Experience working with or integrating business systems as well as engineering/developer tooling Experience creating training materials, documentation, or workshops for mixed technical/non-technical audiences Understanding of data privacy, security, and compliance considerations Experience implementing safeguards in AI systems, including identity and access controls for automated/non-human actors The typical starting salary for this role in the US is $170,000 — $220,000 USD The typical starting salary for this role in US Premium Markets is $200,000 — $250,000 USD Compensation For roles based in the United States , t he typical starting salary range for this position is listed above. In certain locations, such as the San Francisco Bay Area and the New York City Metro Area, a premium market range may apply, as listed. These salary ranges reflect what we reasonably and in good faith believe to be the minimum and maximum pay for this role at the time of posting. The actual compensation may be higher or lower than the amounts listed, and the ranges may be subject to future adjustments. An individual’s placement within the range will depend on various factors, including (but not limited to) education, qualifications, certifications, experience, skills, location, performance, and the needs of the business or organization. If you have any questions or comments about compensation as a candidate, please get in touch with us at paytransparency@clickhouse.com . Perks Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 20 countries. Healthcare - Employer contributions towards your healthcare. Equity in the company - Every new team member who joins our company receives stock options. Time off - Flexible time off in the US, generous entitlement in other countries. A $500 Home office setup if you’re a remote employee. Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. Culture - We All Shape It As